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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F07%3A03129971%21RIV08-AV0-21230___
rdf:type
n5:Vysledek skos:Concept
dcterms:description
In neurology, when a physician needs to examine if a patient suffers from sleep difficulties, he needs to record several biological signals during the patients whole night sleep (polygraphic examination). The diagnosis is based on a manual examination of this recording. This process is of course very time consuming and many different methods assisting the ex- pert have been developed. Unfortunately, none of the methods have proved to be accurate enough and experts often prefer a manual examination of the recording. In this article we look for the best preprocessing methods and the best classification model for distinguishing sleep stages automatically. In neurology, when a physician needs to examine if a patient suffers from sleep difficulties, he needs to record several biological signals during the patients whole night sleep (polygraphic examination). The diagnosis is based on a manual examination of this recording. This process is of course very time consuming and many different methods assisting the ex- pert have been developed. Unfortunately, none of the methods have proved to be accurate enough and experts often prefer a manual examination of the recording. In this article we look for the best preprocessing methods and the best classification model for distinguishing sleep stages automatically. In neurology, when a physician needs to examine if a patient suffers from sleep difficulties, he needs to record several biological signals during the patients whole night sleep (polygraphic examination). The diagnosis is based on a manual examination of this recording. This process is of course very time consuming and many different methods assisting the ex- pert have been developed. Unfortunately, none of the methods have proved to be accurate enough and experts often prefer a manual examination of the recording. In this article we look for the best preprocessing methods and the best classification model for distinguishing sleep stages automatically.
dcterms:title
Application of Inductive Modeling to Sleep Stages Classification Application of Inductive Modeling to Sleep Stages Classification Aplikace induktivního modelování na klasifikaci spánkových fází
skos:prefLabel
Aplikace induktivního modelování na klasifikaci spánkových fází Application of Inductive Modeling to Sleep Stages Classification Application of Inductive Modeling to Sleep Stages Classification
skos:notation
RIV/68407700:21230/07:03129971!RIV08-AV0-21230___
n3:strany
63;70
n3:aktivita
n17:Z n17:P
n3:aktivity
P(KJB201210701), Z(MSM6840770012)
n3:dodaniDat
n8:2008
n3:domaciTvurceVysledku
n7:7035586 n7:1266500 n7:3271404
n3:druhVysledku
n9:D
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
410475
n3:idVysledku
RIV/68407700:21230/07:03129971
n3:jazykVysledku
n6:eng
n3:klicovaSlova
Artifficial neural networks; Classification; EEG; GAME; Inductive modeling; Sleep scoring
n3:klicoveSlovo
n4:Classification n4:EEG n4:GAME n4:Inductive%20modeling n4:Sleep%20scoring n4:Artifficial%20neural%20networks
n3:kontrolniKodProRIV
[889EA6987639]
n3:mistoKonaniAkce
Rožnov pod Radhoštěm
n3:mistoVydani
Ostrava
n3:nazevZdroje
Proceedings of 41th Spring International Conference MOSIS\'07
n3:obor
n11:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n13:KJB201210701
n3:rokUplatneniVysledku
n8:2007
n3:tvurceVysledku
Čepek, Miroslav Šnorek, Miroslav Kordík, Pavel
n3:typAkce
n22:EUR
n3:zahajeniAkce
2007-04-24+02:00
n3:zamer
n19:MSM6840770012
s:numberOfPages
8
n20:hasPublisher
MARQ
n14:isbn
978-80-86840-30-7
n16:organizacniJednotka
21230